Top hadoop books mentioned on stackoverflow.com

Counsels programmers and administrators for big and small organizations on how to work with large-scale application datasets using Apache Hadoop, discussing its capacity for storing and processing large amounts of data while demonstrating best practices for building reliable and scalable distributed systems.

The product of a unique collaboration among four leading scientists in academic research and industry, Numerical Recipes is a complete text and reference book on scientific computing. In a self-contained manner it proceeds from mathematical and theoretical considerations to actual practical computer routines. With over 100 new routines bringing the total to well over 300, plus upgraded versions of the original routines, the new edition remains the most practical, comprehensive handbook of scientific computing available today.

A guide to Apache Cassandra covers such topics as write, update, and read Cassandra data; add or remove nodes from the cluster; use the JMX interface to monitor a cluster's usage; and tune memory settings and data storage for better performance.

Counsels programmers and administrators for big and small organizations on how to work with large-scale application datasets using Apache Hadoop, discussing its capacity for storing and processing large amounts of data while demonstrating best practices for building reliable and scalable distributed systems. Original.

Presents information on machine learning through the use of Apache Mahout, covering such topics as using group data to make individual recommendations, finding logical clusters, and filtering classifications.

You’ve heard the hype about Hadoop: it runs petabyte–scale data mining tasks insanely fast, it runs gigantic tasks on clouds for absurdly cheap, it’s been heavily committed to by tech giants like IBM, Yahoo!, and the Apache Project, and it’s completely open-source (thus free). But what exactly is it, and more importantly, how do you even get a Hadoop cluster up and running? From Apress, the name you’ve come to trust for hands–on technical knowledge, Pro Hadoop brings you up to speed on Hadoop. You learn the ins and outs of MapReduce; how to structure a cluster, design, and implement the Hadoop file system; and how to build your first cloud–computing tasks using Hadoop. Learn how to let Hadoop take care of distributing and parallelizing your software—you just focus on the code, Hadoop takes care of the rest. Best of all, you’ll learn from a tech professional who’s been in the Hadoop scene since day one. Written from the perspective of a principal engineer with down–in–the–trenches knowledge of what to do wrong with Hadoop, you learn how to avoid the common, expensive first errors that everyone makes with creating their own Hadoop system or inheriting someone else’s. Skip the novice stage and the expensive, hard–to–fix mistakes...go straight to seasoned pro on the hottest cloud–computing framework with Pro Hadoop. Your productivity will blow your managers away. What you’ll learn Set up a stand–alone Hadoop cluster the smart way, laid out simply and step by step so you can get up and running quickly to build your next data center, collaborative, data–intensive Internet services application, Software as a Service (SaaS), and more. Optimize your Hadoop production tasks like an experienced pro. Work with time–proven, bulletproof standard patterns that have been tested and debugged in high–volume production. Understand just enough theoretical knowledge to know why something works in Hadoop, without getting bogged down in abstruse walls of theory. Get detailed explanations of not only how to do something with Hadoop, but also why, from a front–line coder with years in the Hadoop game. Turn someone else’s expensive cluster–wide “wrong” into an orderly, productive "right" with professional–level debugging and testing. Who this book is for IT professionals interested in investigating Hadoop and implementing it in their organizations, and existing Hadoop users who want to deepen their professional toolkits. Table of Contents Getting Started with Hadoop Core The Basics of a MapReduce Job The Basics of Multimachine Clusters HDFS Details for Multimachine Clusters MapReduce Details for Multimachine Clusters Tuning Your MapReduce Jobs Unit Testing and Debugging Advanced and Alternate MapReduce Techniques Solving Problems with Hadoop Projects Based On Hadoop and Future Directions

Big data can be difficult to handle using traditional data-bases. Apache Hadoop is a NoSQL applications framework that runs on distributed clusters. This lets it scale to huge datasets. If you need analytic information from your data, Hadoop's the way togo. Hadoop in Action introduces the subject and teachers you how to write programs in the MapReduce style. It starts with a few easy examples and then moves quickly to show Hadoop use in more complex data analysis tasks. included are best practices and design patterns of MapReduce programming.

Counsels programmers and administrators for big and small organizations on how to work with large-scale application datasets using Apache Hadoop, discussing its capacity for storing and processing large amounts of data while demonstrating best practices for building reliable and scalable distributed systems. Original.

Pro Hadoop 2 brings you up to speed on Hadoop – the framework of big data. Revised to cover Hadoop 2.0, the book covers the very latest developments such as YARN (aka MapReduce 2.0), new HDFS high-availability features, and increased scalability in the form of HDFS Federations. All the old content has been revised too, giving the latest on the ins and outs of MapReduce, cluster design, the Hadoop Distributed File System, and more. Pro Hadoop 2 covers everything you need to build your first Hadoop cluster and begin analyzing and deriving value from your business and scientific data. Learn to solve big-data problems the MapReduce way, by breaking a big problem into chunks and creating small-scale solutions that can be flung across thousands upon thousands of nodes to analyze large data volumes in a short amount of wall-clock time. Learn how to let Hadoop take care of distributing and parallelizing your software—you just focus on the code; Hadoop takes care of the rest. Covers all that is new in Hadoop 2.0 Written by a professional involved in Hadoop since day one Takes you quickly to the seasoned pro level on the hottest cloud-computing framework What you’ll learn Build a resilient and scalable Hadoop compute cluster. Analyze large volumes of data in amazingly short time. Optimize Hadoop tasks like a seasoned professional. Implement bulletproof patterns that are proven successful. Scale out using the new HDFS Federations feature set. Chunk large problems into highly-parallel, MapReduce modules Who this book is for Pro Hadoop 2 is aimed at I.T. professionals investigating Hadoop and implementing it in their organizations. Existing Hadoop users will deepen their toolkits and come up to speed on what’s new Hadoop 2.0. New Hadoop users will quickly move to the seasoned professional level in their use of the toolset.

Counsels programmers and administrators for big and small organizations on how to work with large-scale application datasets using Apache Hadoop, discussing its capacity for storing and processing large amounts of data while demonstrating best practices for building reliable and scalable distributed systems.

A comprehensive practical guide that walks you through the multiple stages of data management in enterprise and gives you numerous design patterns with appropriate code examples to solve frequent problems in each of these stages. The chapters are organized to mimick the sequential data flow evidenced in Analytics platforms, but they can also be read independently to solve a particular group of problems in the Big Data life cycle.If you are an experienced developer who is already familiar with Pig and is looking for a use case standpoint where they can relate to the problems of data ingestion, profiling, cleansing, transforming, and egressing data encountered in the enterprises. Knowledge of Hadoop and Pig is necessary for readers to grasp the intricacies of Pig design patterns better.

If your organization is looking for a storage solution to accommodate a virtually endless amount of data, this book will show you how Apache HBase can fulfill your needs. As the open source implementation of Google's BigTable architecture, HBase scales to billions of rows and millions of columns, while ensuring that write and read performance remain constant.HBase: The Definitive Guideprovides the details you require, whether you simply want to evaluate this high-performance, non-relational database, or put it into practice right away. HBase's adoption rate is beginning to climb, and several IT executives are asking pointed questions about this high-capacity database. This is the only book available to give you meaningful answers. Learn how to distribute large datasets across an inexpensive cluster of commodity servers Develop HBase clients in many programming languages, including Java, Python, and Ruby Get details on HBase's primary storage system, HDFS—Hadoop’s distributed and replicated filesystem Learn how HBase's native interface to Hadoop’s MapReduce framework enables easy development and execution of batch jobs that can scan entire tables Discover the integration between HBase and other facets of the Apache Hadoop project

Our world is being revolutionized by data-driven methods: access to large amounts of data has generated new insights and opened exciting new opportunities in commerce, science, and computing applications. Processing the enormous quantities of data necessary for these advances requires large clusters, making distributed computing paradigms more crucial than ever. MapReduce is a programming model for expressing distributed computations on massive datasets and an execution framework for large-scale data processing on clusters of commodity servers. The programming model provides an easy-to-understand abstraction for designing scalable algorithms, while the execution framework transparently handles many system-level details, ranging from scheduling to synchronization to fault tolerance. This book focuses on MapReduce algorithm design, with an emphasis on text processing algorithms common in natural language processing, information retrieval, and machine learning. We introduce the notion of MapReduce design patterns, which represent general reusable solutions to commonly occurring problems across a variety of problem domains. This book not only intends to help the reader "think in MapReduce", but also discusses limitations of the programming model as well. This volume is a printed version of a work that appears in the Synthesis Digital Library of Engineering and Computer Science. Synthesis Lectures provide concise, original presentations of important research and development topics, published quickly, in digital and print formats. For more information visit www.morganclaypool.com